We discuss a speed controller for a hopping robot with a pneumatically
powered leg. The controller uses a neural network to model the
neutral point as a function of running speed and hopping height. The
network is trained off-line using training data taken from a simulated
hopper that is manually controlled by a human. Simulation experiments
of hopping in the sagittal plane show improved performance over a
Raibert PD controller, which uses a linear approximation for the
neutral point.